Search Results (343)

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems. Full article

Background: Chronic obstructive pulmonary disease (COPD) increasingly strains European health systems amid population ageing. Digital health interventions (DHIs) can reduce hospitalizations and support self-management, yet older patients hesitate to adopt them. Tailored interventions require understanding patient profiles. This study aimed to identify clusters by intention to use DHIs. Methods: Between July 2024 and February 2025, 232 COPD patients (mean age 65; 61% female) across seven European countries completed surveys covering sociodemographic and Unified Theory of Technology Acceptance (UTAUT) constructs. Intention to use DHIs was categorized as positive, neutral, or negative. Weighted UTAUT scores were clustered using Gower distance and Partitioning Around Medoids. Associations were visualized with multiple correspondence analysis and heat maps; differences were tested with the chi-square test. Results: Intention to adopt DHIs varied across countries, with the highest in the Netherlands. Two clusters emerged. Cluster 1, the ‘balanced hesitant’ group (n = 104), showed mixed intentions (38% positive, 40% neutral, 21% negative). Barriers included low performance expectancy and limited digital skills (both p < 0.05). Cluster 2, the ‘enthusiastic’ group (n = 128), demonstrated strong adoption intentions, with 84% positive intention. Enablers included low effort expectancy and complex disease (p < 0.01). Across both clusters, performance expectancy predicted intention. Conclusions: Digital health adoption among COPD patients is shaped by psychosocial and digital skill profiles. Hesitant users benefit from expectation-based information about DHIs, digital literacy training and peer support. Enthusiasts require ease of integration. Performance expectancy is a consistent driver of adoption, whereas country-specific factors should guide strategies. Full article

This study analyses the integration of Generative Artificial Intelligence (GenAI) in agro-environmental higher education in Ecuador, focusing on its contribution to sustainable digital transformation aligned with Sustainable Development Goals (SDGs) 4 and 9. The research was conducted at the Faculty of Agricultural and Environmental Engineering (FICAYA) of Universidad Técnica del Norte (UTN) using a quantitative, cross-sectional, and analytical design. A validated digital survey grounded in established technology-acceptance frameworks—the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) was administered to 94% of the student population, showing satisfactory internal consistency (Cronbach’s α = 0.87). Data was analysed using descriptive statistics and multivariate techniques, including Principal Component Analysis (PCA) and k-means clustering. The results obtained in Microsoft Forms^® indicate that ChatGPT-5 is the most widely used GenAI tool (54.2%), followed by Gemini (11.9%). Students reported perceived improvements in academic performance (62.5%), conceptual understanding (74.6%), and task efficiency (69.1%). PCA explained 67% of the total variance, identifying three latent dimensions: effectiveness and satisfaction, institutional access and support, and ethical concerns versus operational benefits. Furthermore, k-means clustering (k = 2) segmented users into two distinct profiles Integrators, characterised by frequent use and positive perceptions, and Cautious Users, exhibiting lower usage and greater ethical or technical concerns. Overall, the findings highlight GenAI as a catalyst for sustainable education and underline the need for institutional and ethical frameworks to support its responsible integration in Latin American universities. Full article

Small and medium enterprises (SMEs) represent a fundamental pillar of economic development in Eastern Europe. Yet, they frequently encounter significant obstacles in accessing financing, stemming from informational asymmetries, elevated risks, the absence of collateral, and adverse regulatory environments. This research examines the primary determinants of adopting blockchain-based supply chain financing platforms, an alternative financing solution that streamlines processes, reduces costs, and enhances transparency and security. The study develops and validates an innovative conceptual model grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to a sample of 200 respondents across seven Eastern European countries, and the model’s hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The research findings demonstrate that supply chain partner readiness constitutes the most influential factor affecting behavioral intention to use blockchain-based supply chain financing platforms. Additionally, performance expectancy, effort expectancy, and perceived trust were identified as significant positive determinants. Furthermore, the study highlights blockchain readiness as a crucial factor influencing actual usage behavior. These findings provide valuable insights and contribute to advancing knowledge through the utilization of an extended UTAUT framework and validation of obtained results through comparison with other relevant studies in the field. Full article

Rapid adoption of digital surveying technologies in construction has highlighted the need for engineering education to equip students with technological competency as well as higher-order problem-solving skills. This experiment explores undergraduate students’ acceptance of emerging surveying technologies and their perceived learning results within a constructivist framework of experiential learning. Thirty-six students in a required construction surveying class interacted with traditional and advanced technologies such as total stations, terrestrial laser scanning, drones, and mobile LiDAR through structured, semi-structured, and unstructured lab activities. Data were gathered based on two post-course surveys: a technology acceptance survey grounded in Unified Theory of Acceptance and Use of Technology (UTAUT) and a self-perceived cognitive learning outcome survey through Bloom’s Taxonomy. Qualitative analysis along with quantitative analysis indicated a gap between technology acceptance and perceived learning gains. Laser scanner had the greatest acceptance scores followed by other advanced tools. Total station (widespread in hands-on lab activities) was perceived to have been most influential in terms of enhancing learning. Lower-order skills were strengthened in structured labs, while higher-order thinking emerged more unevenly in open-ended labs. These findings underscore that the mode of student engagement with technology matters more for learning than the sophistication of the tools themselves. By embedding UTAUT and Bloom’s Taxonomy in an authentic learning environment, this experiment provides engineering educators a mechanism to assess technology-enhanced learning and identifies strategies to facilitate higher-order skills aligned with industry needs. Full article

High cybersecurity risks and attacks cause information theft, unauthorized access to data and information, reputational damage, and financial loss in small and medium enterprises (SMEs). This creates a need to adopt information security systems of SMEs through innovation and compliance with information security policies. This study seeks to develop an integrated research model assessing the adoption of InfoSec systems in SMEs based on three existing theories, namely the technology acceptance model (TAM), theory of reasoned action (TRA), and unified theory of acceptance and use of technology (UTAUT). A thorough review of literature identified prior experience, enjoyment of new InfoSec technology, top management support, IT infrastructure, security training, legal-governmental regulations, and attitude as potential determinants of adoption of InfoSec systems. A self-developed and self-administered questionnaire was distributed to 418 employees, mid-level managers, and top-level managers working in SMEs operating in Riyadh, Saudi Arabia. The study found that prior experience, top management support, IT infrastructure, security training, and legal-governmental regulations have a positive impact on attitude toward InfoSec systems, which in turn positively influences the adoption of InfoSec systems. Gender, education, and occupation significantly moderated the impact of some determinants on attitude and, consequently, adoption of InfoSec systems. Such an integrated framework offers actionable insights and recommendations, including enhancing information security awareness and compliance with information security policies, as well as increasing profitability within SMEs. The study findings make considerable theoretical contributions to the development of knowledge and deliver practical contributions towards the status of SMEs in Saudi Arabia. Full article

The rapid advancement of artificial intelligence (AI) technologies has created both opportunities and challenges in higher education. AI tools are increasingly regarded as potentially transformative, yet also contested, resources for enhancing teaching and learning. This study investigates the behavioral intentions and use behavior of Generative AI (GenAI) tools among Portuguese university students by applying the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. Seven predictors (performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value) were included to develop a model explaining the behavioral intention and use behavior of AI tools. Data were collected from 323 Portuguese university students and analyzed using partial least squares structural equation modeling. The results show that only hedonic motivation and habit significantly influence behavioral intention, with habit emerging as the strongest predictor. Behavioral intention, in turn, positively affects use behavior. Moreover, gender also moderates the relationship between habit and behavioral intention. These findings deepen our understanding of GenAI tools’ acceptance in higher education and underscore the central roles of enjoyment and routine in adoption processes. They further provide insights to support institutional strategies, inform curriculum design, and support the ethical integration of GenAI technologies in academic contexts. Full article

County-wide distributed rooftop photovoltaic (DRPV) systems, as an emerging form of renewable energy development, constitute a critical component for the low-carbon energy transition and carbon reduction. However, the pilot implementation in China has faced many challenges, with resistance from rural residents being a key issue requiring urgent resolution. This study aimed to investigate the underlying factors influencing their participation in DRPV and identify the key determinants. The topic modeling and evolutionary analysis were first conducted based on the multi-platform online textual data. The theoretical model was constructed combining the antecedent variables identified by the online textual analysis and the classic Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This model was validated through questionnaire surveys and structural equation modeling. The results revealed that facilitating conditions were the core determinant of rural residents’ participation in DRPV systems. Government-led safeguard mechanisms served as the primary enhancer of perceived convenience. Additionally, effort expectancy (0.301), performance expectancy (0.253), and social influence (0.424) all positively correlated with participation intention, with social influence exhibiting the strongest impact. Notably, rural residents equally prioritize environmental benefits and economic returns from DRPV systems. These findings provided policy insights for promoting DRPV projects in the future. Full article

This study examines the effects of trust in AI, willingness to accept AI, and perceptions of AI performance on brand loyalty in the hospitality industry. The study further posits that purchase intentions mediate the relationship between understudied independent variables and brand loyalty. Drawing on the Technology Acceptance Model, Unified Theory of Acceptance Use of Technology UTAUT, and Trust–Satisfaction theory as the theoretical foundation, the study posits seven hypotheses. Data were collected via an online platform; ultimately, 183 participants participated in the survey. The study tests the hypotheses using Hayes’s Macro PROCESS Model 4 mediation, where the direct and indirect effects were established. The study observes that the direct and indirect effects of trust in AI and the perceived performance of AI on brand loyalty were significant. However, only the indirect effect was significant for the willingness to accept AI on brand loyalty. These observations show that the use of AI-enabled services enhances a stronger intention to book, leading to an improved brand loyalty. The results extend the body of knowledge in this area by linking the unified framework of trust in AI, AI perceived performance, and willingness to accept AI to brand loyalty in the hospitality industry. For managers and hoteliers, this finding is clear, emphasising that the transparent and reliable use of AI enhances purchase intentions and improves brand loyalty. Full article

This study examines the readiness of the accounting and auditing profession to adopt disruptive innovations, with a particular focus on sustainable digital transformation. It investigates the factors influencing auditors’ and accountants’ intention to adopt blockchain technology (BT) as a sustainable digital infrastructure that enhances transparency, accountability, traceability, and operational efficiency. The research integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with the third iteration of the Technology Acceptance Model (TAM3), providing a comprehensive framework for understanding the sustainable adoption of emerging technologies. A quantitative research design was employed through an online questionnaire, collecting empirical data from 394 prospective and existing blockchain users within the accounting and auditing professions in Saudi Arabia. Data validation and hypothesis testing were conducted using Structural Equation Modeling (SEM) with Smart-PLS software (version 4.1.0.8). The results reveal a strong and significant positive influence of performance expectancy (PE), effort expectancy (EE), and social influence (SI) on intention to use (IU). Additionally, PE is positively and significantly associated with job relevance (JR) and output quality (OQ). Conversely, computer self-efficacy (CSE) shows no significant impact on EE, while compatibility (CO) positively influences EE but not IU. Moreover, EE has a substantial effect on PE. These findings contribute to the growing discourse on how disruptive ICTs are reshaping the accounting and auditing profession while supporting sustainable digital transformation. The study provides practical insights for policymakers, regulators, corporate leaders, and blockchain providers seeking to leverage BT not only for technological efficiency but also to advance long-term organizational sustainability and responsible governance. Full article

Due to the limitations of general-purpose generative artificial intelligence (GenAI) platforms in meeting the needs of fashion design, AI-based integrated fashion design platforms (AIIFDP) have emerged as a more suitable solution. As the next generation of designers, fashion design students play a pivotal role in shaping the optimization and promotion of AIIFDP. However, research on their continuance intention toward such platforms remains limited. This study constructs an integrated model by combining the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Expectation-Confirmation Model (ECM), and extending it with variables such as personal innovativeness, habit, and perceived intelligence. Using a multi-stage SEM-ANN analysis, the study empirically analyzed data from 486 questionnaires completed by fashion design students in China. The results suggest that satisfaction is the most significant positive factor influencing continuance intention. Moreover, performance expectancy, social influence, perceived intelligence, and habit also exert significant effects. This study broadens the segmented perspective on the application of GenAI in design education and validates the applicability of the extended UTAUT-ECM model in the context of AIIFDP. It also provides theoretical foundations and multi-level strategic recommendations for optimizing AIIFDP products and guiding their integration into educational practices. Full article

This study aims to examine the factors influencing the acceptance of extruded pellet (EP) usage among Korea’s olive flounder farming households by analyzing their acceptance factors to provide recommendations for its wider adoption. A survey was conducted among olive flounder farming households, and 188 valid questionnaires were collected. To examine the factors influencing EP acceptance intention, the UTAUT2 (extended unified theory of acceptance and use of technology) model was used. The independent variables were categorized into performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and reliability as independent variables, while acceptance intention was considered as the dependent variable, to derive measurement items. In addition, the differences between the two groups were analyzed by using the aquaculture region and the manager’s experience as moderating variables. The hypothesis testing showed that performance expectancy, effort expectancy, social influence, price value, and reliability factors had a positive effect on acceptance intention, while facilitating conditions did not show a significant effect. The analysis of the moderating effect of the aquaculture region indicated a significant difference between the Jeju-do and Jeollanam-do groups. Conversely, the moderating effect of experience showed no significant difference between those with more experience (≥10 years) and those with less (<10 years). Full article

This study used Q methodology to explore and categorise caregivers’ subjective perceptions of artificial intelligence (AI)-powered ‘virtual human’ (AVH) devices in caring for older adults. We derived 123 initial statements from literature and focus groups and narrowed them to 34 statements as the final Q sample. Seventeen caregivers, nurses, and social workers completed the Q-sorting procedure. Using principal component analysis and Varimax rotation in Ken-Q, we identified three perception types: Active Acceptors, who emphasise the devices’ practical utility in patient communication; Improvement Seekers, who conditionally accept the technology while seeking greater accuracy and effectiveness; and Emotional Support Seekers, who view the device as a tool for emotional relief and psychological support. These findings suggest that technology acceptance in caregiving extends beyond functional utility. It also involves trust, affective experience, and interpersonal interaction. This study integrates multiple frameworks, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Science and Technology Studies (STS), and Human–Machine Communication (HMC) theory, to provide a multifaceted understanding of caregivers’ acceptance of AI technology. The results offer valuable implications for designing user-centred AI care devices and enhanced emotional and communicative functions. Full article

This study empirically examines the impact of artificial intelligence (AI) service competency on digital economic activities, digital social participation, and daily life satisfaction. It also investigates the mediating role of e-commerce utilization in these relationships. The ultimate aim is to provide practical implications for enhancing citizens’ quality of life and promote their active participation in the digital society. This study employed data from 7001 respondents drawn from the 2023 Digital Information Gap Survey. The analysis was conducted using SPSS 29.0 and structural equation modeling (SEM) to examine the structural relationships between variables. The results revealed that perceived usefulness of AI technology had a significant positive effect on digital economic activities, while digital information competency positively influenced both digital economic activities and daily life satisfaction. In contrast, attitude toward AI technology did not have a significant effect on digital economic activities and even showed a negative association with digital social participation. Furthermore, digital information competency demonstrated a significant indirect effect on the overall digital outcomes through the mediating role of the level of e-commerce utilization. This study applies an integrated theoretical framework combining the Theory of Reasoned Action (TRA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). It goes beyond theoretical interpretation by delivering practical implications that offer academic and policy value, particularly for developing policy strategies such as reducing the digital divide. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied in breast imaging, with potential to improve diagnostic accuracy and reduce workload in mammographic breast cancer screening. However, real-world integration of AI into national screening programs remains limited, and little is known about radiologists’ perspectives in Asian settings. This study aimed to explore radiologists’ perceptions of AI adoption in Singapore’s breast screening program, focusing on perceived benefits, barriers, and requirements for safe integration. Methods: We conducted a mixed methods study involving a cross-sectional survey of 17 radiologists with prior experience using AI-assisted mammography, followed by semi-structured interviews with 10 radiologists across all three public healthcare clusters. The survey measured confidence in AI, attitudes toward its diagnostic role, and integration preferences. Interviews were analyzed thematically, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Results: Among survey respondents, 64.7% recommended AI as a companion reader, though only 29.4% rated its performance as comparable to humans. Confidence was highest when AI was validated on local datasets (mean 9.3/10). Interviews highlighted AI’s strengths in routine, fatigue-prone tasks, but skepticism for complex cases. Concerns included false positives, workflow inefficiencies, medico-legal accountability, and long-term costs. Radiologists emphasized the importance of national guidelines, local validation, and clear role definition to build trust. Conclusions: Radiologists support AI as an adjunct to, but not a replacement for, human readers in breast cancer screening. Adoption will require robust regulatory frameworks, seamless workflow integration, transparent validation on local data, and structured user training to ensure safe and effective implementation. Full article